cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Agung Suharyanto
Contact Email
suharyantoagung@gmail.com
Phone
+628126493527
Journal Mail Official
suharyantoagung@gmail.com
Editorial Address
Perumahan Griya Nafisa 2, Blok A no 10, Percut Sei Tuan, Deli Serdang
Location
Unknown,
Unknown
INDONESIA
INCODING: Journal of Informatics and Computer Science Engineering
Published by Mahesa Research Center
ISSN : -     EISSN : 2776432X     DOI : 10.34007
Core Subject : Science,
Computer Science & IT
INCODING: Journal of Informatics and computer science engineering, is a journal of informatics is the study of the structure, behavior, and interactions of natural and engineered computational systems.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 65 Documents
 3   4   5   6   7 
Pengembangan Hybrid App Arsip Ijazah dan SKHUN di SMK Pembangunan Bukittinggi Khomarudin, Agus Nur; Novita, Rina; Aulia, Romy; Putri, Ega Evinda; Jamaluddin, Jamaluddin
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 1 (2025): INCODING APRIL
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i1.770

Abstract

The importance of fast, precise, and accurate information is highly essential for every institution or organization, particularly in archival management. SMK Pembangunan Bukittinggi is currently making continuous efforts to support the "Innovative Vocational School" program. However, the institution faces several challenges in managing the archiving of diplomas and SKHUN (School Leaving Certificates). The current archiving process is still carried out using conventional methods that are not yet digital-based, which presents several weaknesses, such as the risk of damage, fire, and difficulties in retrieving required documents. This research follows the development steps outlined in the Agile methodology. The research has produced a hybrid application for diploma and SKHUN archiving at SMK Pembangunan Bukittinggi, which has undergone several tests, including system validity testing, which resulted in a score of 0.83, indicating a valid system; practicality testing with a score of 0.90, categorized as very practical; and effectiveness testing with a score of 0.82, indicating high effectiveness. Therefore, it can be concluded that the hybrid archiving application for diplomas and SKHUN developed in this study is feasible to be implemented at SMK Pembangunan Bukittinggi..
Penggunaan Algoritma Fuzzy C-Means untuk Optimalisasi Pengelompokan Data Cuaca dalam Prediksi Curah Hujan di Indonesia Hidayah, Safrina; Muliono, Rizki
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.839

Abstract

This study develops an information system to optimize rainfall data clustering in Indonesia using the Fuzzy C-Means method. Rainfall clustering aims to provide accurate information about climatic conditions by categorizing regions into three rainfall levels: high, medium, and low. The data used in this study were obtained from observations by the Meteorology, Climatology, and Geophysics Agency (BMKG) from 2011 to 2015 across various provinces. The Fuzzy C-Means method was selected due to its ability to handle uncertainty by assigning membership degrees to each cluster. The resulting clustering information is expected to assist the community and relevant sectors such as agriculture, fisheries, and regional planning in predicting rainfall and making informed decisions. The developed system can also be extended to process other weather data, including air quality and wind speed.
Klasifikasi Penyakit Tanaman Cabai Menggunakan Googlenet Pada Citra Daun Harahap, Jaffar Siddik; Sembiring, Arnes
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 1 (2025): INCODING APRIL
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i1.832

Abstract

Red chili pepper (Capsicum annuum L.) is a horticultural commodity that has high economic value, but its production is often hampered by plant disease attacks. To automatically detect diseases in chili leaves, this study uses a deep learning approach with GoogLeNet architecture and transfer learning techniques. This study aims to classify five types of chili leaf diseases, namely Healthy, Leaf Curl, Leaf Spot, Whitefly, and Yellowish, using a model initialized with pretrained weights from ImageNet. Three types of optimizers (Adam, RMSprop, and SGD) were tested to evaluate their effect on classification accuracy. The results showed that Adam performed best with a validation accuracy of 98.80%, followed by RMSprop (98.40%) and SGD (94.00%). The confusion matrix shows that misclassification occurs mainly in the Leaf Curl class, which is often confused with Yellowish, due to visual similarities. Although the classification results were excellent, limitations such as the small size of the dataset (500 images) and the need for further augmentation techniques to address prediction errors remained challenges. This research contributes to the development of an efficient and accurate computer vision-based plant disease classification system.
Penerapan Data Mining Menggunakan Algoritma K-Medoids Dalam Pengelompokan Nasabah Penerima Reward Pada PT Dotri Gadai Jaya Zebua, Meniati; Muliono, Rizki
INCODING: Journal of Informatics and Computer Science Engineering Vol 4, No 2 (2024): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v4i2.709

Abstract

The increasing growth of the financial industry makes companies experience intense competitive pressure. PT Dotri Gadai Jaya (PT DGJ) is a private pawnshop company, facing challenges in maintaining and increasing customer loyalty amidst this competition. One of the strategies used by PT DGJ is to provide rewards to customers based on the number of pawn loan transactions. However, companies experience difficulties in grouping reward recipient customers efficiently and accurately. To overcome this problem, it is necessary to apply data mining using the K-Medoids algorithm. The main objective of this research is to apply the K-Medoids algorithm in grouping reward recipient customers at PT DGJ, knowing the grouping results, and evaluating the results using the Davies Bouldin Index (DBI). The results of the grouping of 1,085 customers are 314 customers who received a 30% reward, 540 customers with a 20% reward and 231 customers with a 10% reward. The cluster evaluation result using DBI is 0.368812, which means the cluster quality value is close to 0 or is quite small. So it can be said that the resulting cluster is quite good.
Analisis Sentimen Produk Berdasarkan Review Pelanggan Shopee Menggunakan KNN Irwannia, Fira; Lubis, Andre Hasudungan
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.865

Abstract

This study aims to conduct sentiment analysis on customer reviews of mukena products available on the Shopee application using the K-Nearest Neighbors (KNN) algorithm. The data used is primary data consisting of 200 reviews collected manually. The analysis process begins with data preprocessing such as case folding, tokenization, stopword removal, and stemming, followed by feature extraction using the TF-IDF method, and classification using the KNN algorithm. The model's performance is evaluated using a confusion matrix. The results show that the proportion of training data and the n_neighbors parameter significantly affect the model's accuracy. A 90% training and 10% testing proportion produced the highest accuracy of 90%. However, with n_neighbors = 3, the best performance was achieved with a 70:30 data split, reaching 81.67% accuracy. This study demonstrates that KNN is an effective method for sentiment analysis on product reviews.
Analisis Klustering Menggunakan Algoritma DBSCAN untuk Deteksi Anomali dalam Data Transaksi Keuangan Alwi, Buchori; Muliono, Rizki
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 1 (2025): INCODING APRIL
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i1.827

Abstract

Anomaly detection in financial transaction data is a crucial aspect due to the increasing use of e-money, which raises the risk of suspicious activities such as fraud and money laundering. This study applies the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster transaction data and identify anomalies based on three main variables: transaction amount, transaction frequency, and final balance. The optimal parameters were determined by evaluating various combinations of epsilon (ε) and minPts values using the Davies-Bouldin Index (DBI) as a clustering quality indicator. The analysis results indicate that the optimal parameters are ε of 0.2727 and minPts of 6, with a DBI score of 1.1753. DBSCAN successfully formed six main clusters and detected 138 data points as noise, indicating potentially abnormal transactions. These findings demonstrate that DBSCAN can effectively distinguish between normal and suspicious data without requiring prior assumptions on the number of clusters, contributing to the development of more accurate and adaptive digital transaction anomaly detection systems.
Perancangan Sistem Informasi Manajemen Dalam Pengelolaan Data Kepegawain Di Kantor Dinas Perkebunan Provinsi Sumatera Utara Darkani, M. Farhan; Muliono, Rizki
INCODING: Journal of Informatics and Computer Science Engineering Vol 4, No 2 (2024): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v4i2.731

Abstract

The use of this information system will provide convenience for the North Sumatra Plantation Service in managing employees, especially in terms of inputting employee data. In connection with the data input mechanism still using the classic method, namely using MS Excel, which the author considers to be less flexible. Therefore, through this Internship, the author hopes to be able to design a web application where employees can input their data more easily and flexibly. The creation of this system starts from data collection, system analysis, system design and implementation.
Analisis Pengaruh Fungsi Aktivasi CNN terhadap Performa Klasifikasi Hewan Ray, Raja Pahlefi; Sembiring, Arnes
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.847

Abstract

This study aims to analyze the impact of five activation functions ReLU, LeakyReLU, ELU, Sigmoid, and Tanh—on the performance of a Convolutional Neural Network (CNN) model for image classification into three categories: cats, dogs, and wild animals. The evaluation was conducted using validation accuracy metrics, accuracy trends across training epochs, and confusion matrix analysis. The results show that modern activation functions such as LeakyReLU, ELU, and ReLU yield high accuracy and balanced predictions, demonstrating their effectiveness in mitigating vanishing gradient issues and enhancing the model's generalization capability. In contrast, classical functions like Sigmoid and Tanh performed poorly, producing imbalanced predictions and stagnant accuracy Therefore, the choice of activation function plays a critical role in building an optimal CNN model for image classification tasks. This study recommends ReLU-based activation functions, particularly LeakyReLU, as the primary choice for developing multi-class image classification models.
Klasifikasi Tumbuhan Obat Berdasarkan Citra Daun Menggunakan Algoritma CNN Sinaga, Nicolas Novelico; Sembiring, Arnes
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 1 (2025): INCODING APRIL
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i1.833

Abstract

This study aims to classify various types of medicinal plants based on leaf images by utilizing the Convolutional Neural Network (CNN) algorithm. The model used is the MobileNetV2 architecture because of its ability to balance accuracy and computational efficiency. The leaf images dataset is divided into training and validation data, then processed through several stages such as augmentation, fine-tuning, and regularization. The evaluation results show that the model successfully achieved the highest validation accuracy of 98,43%, proving that this approach is effective in identifying types of medicinal plants.
Pembuatan Sistem Absensi Siswa Praktek Kerja Lapangan (PKL) Berbasis Web di CV Sae Akademi Digital Medan Syuhada, Rahmad; Muhathir, Muhathir
INCODING: Journal of Informatics and Computer Science Engineering Vol 4, No 2 (2024): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v4i2.729

Abstract

The manual attendance system used in CV SAE Akademi Digital Medan in recording the attendance of Field Work Practice (PKL) students has several shortcomings, such as lack of efficiency, vulnerability to recording errors, and difficulty in real-time monitoring. This research aims to design and implement a more modern and efficient web-based attendance system. The research methods used include needs analysis, system design, development, implementation, and testing. The system is designed using photo upload technology to validate student attendance based on time and location digitally. The study results show that this web-based attendance system has succeeded in increasing the efficiency, accuracy, and transparency of student attendance management. In addition, the dashboard monitoring feature makes it easier for supervisors to monitor student attendance in real time. This system is expected to not only be a solution for CV SAE Digital Academy Medan but also a model for implementing a modern attendance system in other educational institutions.

Page 6 of 7 | Total Record : 65

 3   4   5   6   7 

Filter by Year

2021 2025


Reset
Filter By Issues
All Issue Vol 5, No 2 (2025): INCODING OKTOBER Vol 5, No 1 (2025): INCODING APRIL Vol 4, No 2 (2024): INCODING OKTOBER Vol 4, No 1 (2024): INCODING APRIL Vol 3, No 2 (2023): INCODING OKTOBER Vol 3, No 1 (2023): INCODING APRIL Vol 2, No 2 (2022): INCODING OKTOBER Vol 2, No 1 (2022): INCODING APRIL Vol 1, No 2 (2021): INCODING OKTOBER Vol 1, No 1 (2021): INCODING APRIL